Bayesian tactile face

Zheshen Wang, Xinyu Xu, Baoxin Li

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Scopus citations

Abstract

Computer users with visual impairment cannot access the rich graphical contents in print or digital media unless relying on visual-to-tactile conversion, which is done primarily by human specialists. Automated approaches to this conversion are an emerging research field, in which currently only simple graphics such as diagrams are handled. This paper proposes a systematic method for automatically converting a human portrait image into its tactile form. We model the face based on deformable Active Shape Model (ASM)[4], which is enriched by local appearance models in terms of gradient profiles along the shape. The generic face model including the appearance components is learnt from a set of training face images. Given a new portrait image, the prior model is updated through Bayesian inference. To facilitate the incorporation of a pose-dependent appearance model, we propose a statistical sampling scheme for the inference task. Furthermore, to compensate for the simplicity of the face model, edge segments of a given image are used to enrich the basic face model in generating the final tactile printout. Experiments are designed to evaluate the performance of the proposed method.

Original languageEnglish (US)
Title of host publication26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR
DOIs
StatePublished - Sep 23 2008
Event26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR - Anchorage, AK, United States
Duration: Jun 23 2008Jun 28 2008

Publication series

Name26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR

Other

Other26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR
Country/TerritoryUnited States
CityAnchorage, AK
Period6/23/086/28/08

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Control and Systems Engineering

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